





"""simple docstring"""





import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm
       import torch_xla.debug.metrics as met

class 						lowercase   (   _UpperCAmelCase   ):





      def __init__(			self ,		*lowercase ,		lowercase=None ,		lowercase=None ,		**lowercase					)						->	int:
            super().__init__(*lowercase ,		**lowercase					)
            lowerCAmelCase    		=			eval_examples
            lowerCAmelCase    		=			post_process_function





      def        _snake_case     (			self ,		lowercase = None ,		lowercase=None ,		lowercase = None ,		lowercase = "eval" ,		**lowercase ,		)						->	Dict[str, float]:
            lowerCAmelCase    		=			gen_kwargs.copy()
            lowerCAmelCase    		=			(
                gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""					) is not None else self.args.generation_max_length
            )
            lowerCAmelCase    		=			(
                gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""					) is not None else self.args.generation_num_beams
            )
            lowerCAmelCase    		=			gen_kwargs

            lowerCAmelCase    		=			self.eval_dataset if eval_dataset is None else eval_dataset
            lowerCAmelCase    		=			self.get_eval_dataloader(lowercase					)
            lowerCAmelCase    		=			self.eval_examples if eval_examples is None else eval_examples

            # Temporarily disable metric computation, we will do it in the loop here.
            lowerCAmelCase    		=			self.compute_metrics
            lowerCAmelCase    		=			None
            lowerCAmelCase    		=			time.time()
            lowerCAmelCase    		=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
            try:
                  lowerCAmelCase    		=			eval_loop(
                      lowercase ,		description="""Evaluation""" ,		prediction_loss_only=True if compute_metrics is None else None ,		ignore_keys=lowercase ,		metric_key_prefix=lowercase ,		)
            finally:
                  lowerCAmelCase    		=			compute_metrics
            lowerCAmelCase    		=			self.args.eval_batch_size * self.args.world_size
            if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
                  start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
            output.metrics.update(
                speed_metrics(
                    lowercase ,		lowercase ,		num_samples=output.num_samples ,		num_steps=math.ceil(output.num_samples / total_batch_size					) ,		)					)

            if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
                  # Only the main node write the results by default
                  lowerCAmelCase    		=			self.post_process_function(lowercase ,		lowercase ,		lowercase					)
                  lowerCAmelCase    		=			self.compute_metrics(lowercase					)

                  # Prefix all keys with metric_key_prefix + '_'
                  for key in list(metrics.keys()					):
                        if not key.startswith(f'{metric_key_prefix}_'					):
                              lowerCAmelCase    		=			metrics.pop(lowercase					)

                  metrics.update(output.metrics					)
            else:
                  lowerCAmelCase    		=			output.metrics

            if self.args.should_log:
                  # Only the main node log the results by default
                  self.log(lowercase					)

            if self.args.tpu_metrics_debug or self.args.debug:
                  # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                  xm.master_print(met.metrics_report()					)

            lowerCAmelCase    		=			self.callback_handler.on_evaluate(self.args ,		self.state ,		self.control ,		lowercase					)
            return metrics





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase=None ,		lowercase = "test" ,		**lowercase					)						->	int:
            lowerCAmelCase    		=			gen_kwargs.copy()

            lowerCAmelCase    		=			self.get_test_dataloader(lowercase					)

            # Temporarily disable metric computation, we will do it in the loop here.
            lowerCAmelCase    		=			self.compute_metrics
            lowerCAmelCase    		=			None
            lowerCAmelCase    		=			time.time()
            lowerCAmelCase    		=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
            try:
                  lowerCAmelCase    		=			eval_loop(
                      lowercase ,		description="""Prediction""" ,		prediction_loss_only=True if compute_metrics is None else None ,		ignore_keys=lowercase ,		metric_key_prefix=lowercase ,		)
            finally:
                  lowerCAmelCase    		=			compute_metrics

            lowerCAmelCase    		=			self.args.eval_batch_size * self.args.world_size
            if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
                  start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
            output.metrics.update(
                speed_metrics(
                    lowercase ,		lowercase ,		num_samples=output.num_samples ,		num_steps=math.ceil(output.num_samples / total_batch_size					) ,		)					)
            if self.post_process_function is None or self.compute_metrics is None:
                  return output

            lowerCAmelCase    		=			self.post_process_function(lowercase ,		lowercase ,		lowercase ,		"""predict"""					)
            lowerCAmelCase    		=			self.compute_metrics(lowercase					)

            # Prefix all keys with metric_key_prefix + '_'
            for key in list(metrics.keys()					):
                  if not key.startswith(f'{metric_key_prefix}_'					):
                        lowerCAmelCase    		=			metrics.pop(lowercase					)
            metrics.update(output.metrics					)
            return PredictionOutput(predictions=predictions.predictions ,		label_ids=predictions.label_ids ,		metrics=lowercase					)













"""simple docstring"""





import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int = 8     ):







      '''simple docstring'''




      lowerCAmelCase    		=			ascii_letters + digits + punctuation
      return "".join(secrets.choice(SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      i -= len(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			i // 3
      lowerCAmelCase    		=			i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      lowerCAmelCase    		=			(
          chars_incl
          + random(SCREAMING_SNAKE_CASE				,		quotient + remainder     )
          + random(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
          + random(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      )
      lowerCAmelCase    		=			list(SCREAMING_SNAKE_CASE     )
      shuffle(SCREAMING_SNAKE_CASE     )
      return "".join(SCREAMING_SNAKE_CASE     )

      # random is a generalised function for letters, characters and numbers


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      return "".join(secrets.choice(SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int = 8     ):







      '''simple docstring'''




      if len(SCREAMING_SNAKE_CASE     ) < min_length:
            # Your Password must be at least 8 characters long
            return False

      lowerCAmelCase    		=			any(char in ascii_uppercase for char in password     )
      lowerCAmelCase    		=			any(char in ascii_lowercase for char in password     )
      lowerCAmelCase    		=			any(char in digits for char in password     )
      lowerCAmelCase    		=			any(char in punctuation for char in password     )

      return upper and lower and num and spec_char
      # Passwords should contain UPPERCASE, lowerase
      # numbers, and special characters


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			int(input("""Please indicate the max length of your password: """     ).strip()     )
      lowerCAmelCase    		=			input(
          """Please indicate the characters that must be in your password: """     ).strip()
      print("""Password generated:"""				,		password_generator(SCREAMING_SNAKE_CASE     )     )
      print(
          """Alternative Password generated:"""				,		alternative_password_generator(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		)
      print("""[If you are thinking of using this passsword, You better save it.]"""     )


if __name__ == "__main__":
       main()






